Overview

Brought to you by YData

Dataset statistics

Number of variables9
Number of observations700
Missing cells76
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory49.3 KiB
Average record size in memory72.2 B

Variable types

Numeric7
Categorical2

Alerts

address is highly overall correlated with ageHigh correlation
age is highly overall correlated with address and 2 other fieldsHigh correlation
creddebt is highly overall correlated with debtinc and 2 other fieldsHigh correlation
debtinc is highly overall correlated with creddebt and 1 other fieldsHigh correlation
employ is highly overall correlated with age and 1 other fieldsHigh correlation
income is highly overall correlated with age and 3 other fieldsHigh correlation
othdebt is highly overall correlated with creddebt and 2 other fieldsHigh correlation
default is highly imbalanced (57.1%) Imbalance
age has 19 (2.7%) missing values Missing
ed has 20 (2.9%) missing values Missing
income has 37 (5.3%) missing values Missing
employ has 62 (8.9%) zeros Zeros
address has 50 (7.1%) zeros Zeros

Reproduction

Analysis started2025-10-06 16:37:10.302204
Analysis finished2025-10-06 16:37:18.647992
Duration8.35 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

age
Real number (ℝ)

High correlation  Missing 

Distinct38
Distinct (%)5.6%
Missing19
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean34.898678
Minimum20
Maximum136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2025-10-06T16:37:18.799884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile23
Q128
median34
Q340
95-th percentile49
Maximum136
Range116
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.8618489
Coefficient of variation (CV)0.25393079
Kurtosis23.682151
Mean34.898678
Median Absolute Deviation (MAD)6
Skewness2.4186508
Sum23766
Variance78.532366
MonotonicityNot monotonic
2025-10-06T16:37:19.001280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
29 43
 
6.1%
28 37
 
5.3%
31 36
 
5.1%
41 33
 
4.7%
39 33
 
4.7%
34 33
 
4.7%
36 28
 
4.0%
40 27
 
3.9%
27 27
 
3.9%
35 26
 
3.7%
Other values (28) 358
51.1%
ValueCountFrequency (%)
20 2
 
0.3%
21 10
 
1.4%
22 12
 
1.7%
23 18
2.6%
24 24
3.4%
25 20
2.9%
26 21
3.0%
27 27
3.9%
28 37
5.3%
29 43
6.1%
ValueCountFrequency (%)
136 1
 
0.1%
56 1
 
0.1%
55 2
 
0.3%
54 4
 
0.6%
53 5
 
0.7%
52 6
 
0.9%
51 6
 
0.9%
50 7
1.0%
49 4
 
0.6%
48 15
2.1%

ed
Categorical

Missing 

Distinct5
Distinct (%)0.7%
Missing20
Missing (%)2.9%
Memory size5.6 KiB
1.0
363 
2.0
192 
3.0
84 
4.0
 
36
5.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2040
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 363
51.9%
2.0 192
27.4%
3.0 84
 
12.0%
4.0 36
 
5.1%
5.0 5
 
0.7%
(Missing) 20
 
2.9%

Length

2025-10-06T16:37:19.191103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T16:37:19.352417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 363
53.4%
2.0 192
28.2%
3.0 84
 
12.4%
4.0 36
 
5.3%
5.0 5
 
0.7%

Most occurring characters

ValueCountFrequency (%)
. 680
33.3%
0 680
33.3%
1 363
17.8%
2 192
 
9.4%
3 84
 
4.1%
4 36
 
1.8%
5 5
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2040
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 680
33.3%
0 680
33.3%
1 363
17.8%
2 192
 
9.4%
3 84
 
4.1%
4 36
 
1.8%
5 5
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2040
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 680
33.3%
0 680
33.3%
1 363
17.8%
2 192
 
9.4%
3 84
 
4.1%
4 36
 
1.8%
5 5
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2040
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 680
33.3%
0 680
33.3%
1 363
17.8%
2 192
 
9.4%
3 84
 
4.1%
4 36
 
1.8%
5 5
 
0.2%

employ
Real number (ℝ)

High correlation  Zeros 

Distinct32
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3885714
Minimum0
Maximum31
Zeros62
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2025-10-06T16:37:19.525554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q312
95-th percentile21.05
Maximum31
Range31
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.658039
Coefficient of variation (CV)0.79370356
Kurtosis0.23267552
Mean8.3885714
Median Absolute Deviation (MAD)5
Skewness0.83115358
Sum5872
Variance44.329483
MonotonicityNot monotonic
2025-10-06T16:37:19.711046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 62
 
8.9%
1 49
 
7.0%
4 47
 
6.7%
6 46
 
6.6%
9 45
 
6.4%
2 44
 
6.3%
3 42
 
6.0%
7 38
 
5.4%
5 36
 
5.1%
8 31
 
4.4%
Other values (22) 260
37.1%
ValueCountFrequency (%)
0 62
8.9%
1 49
7.0%
2 44
6.3%
3 42
6.0%
4 47
6.7%
5 36
5.1%
6 46
6.6%
7 38
5.4%
8 31
4.4%
9 45
6.4%
ValueCountFrequency (%)
31 3
 
0.4%
30 2
 
0.3%
29 1
 
0.1%
28 1
 
0.1%
27 2
 
0.3%
26 1
 
0.1%
25 3
 
0.4%
24 4
 
0.6%
23 5
 
0.7%
22 13
1.9%

address
Real number (ℝ)

High correlation  Zeros 

Distinct31
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2685714
Minimum0
Maximum34
Zeros50
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2025-10-06T16:37:19.889513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q312
95-th percentile22
Maximum34
Range34
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.8216088
Coefficient of variation (CV)0.82500452
Kurtosis0.33295333
Mean8.2685714
Median Absolute Deviation (MAD)5
Skewness0.94300083
Sum5788
Variance46.534347
MonotonicityNot monotonic
2025-10-06T16:37:20.075602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2 59
 
8.4%
1 57
 
8.1%
0 50
 
7.1%
4 49
 
7.0%
3 48
 
6.9%
6 43
 
6.1%
8 40
 
5.7%
9 39
 
5.6%
7 34
 
4.9%
5 34
 
4.9%
Other values (21) 247
35.3%
ValueCountFrequency (%)
0 50
7.1%
1 57
8.1%
2 59
8.4%
3 48
6.9%
4 49
7.0%
5 34
4.9%
6 43
6.1%
7 34
4.9%
8 40
5.7%
9 39
5.6%
ValueCountFrequency (%)
34 1
 
0.1%
31 2
 
0.3%
29 1
 
0.1%
27 3
 
0.4%
26 7
1.0%
25 7
1.0%
24 3
 
0.4%
23 9
1.3%
22 7
1.0%
21 9
1.3%

income
Real number (ℝ)

High correlation  Missing 

Distinct113
Distinct (%)17.0%
Missing37
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean45.74359
Minimum14
Maximum446
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2025-10-06T16:37:20.280895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile17
Q124
median34
Q354.5
95-th percentile113
Maximum446
Range432
Interquartile range (IQR)30.5

Descriptive statistics

Standard deviation37.44108
Coefficient of variation (CV)0.81849894
Kurtosis25.724563
Mean45.74359
Median Absolute Deviation (MAD)12
Skewness3.8559903
Sum30328
Variance1401.8345
MonotonicityNot monotonic
2025-10-06T16:37:20.501491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 23
 
3.3%
22 22
 
3.1%
18 21
 
3.0%
20 21
 
3.0%
25 21
 
3.0%
26 20
 
2.9%
28 20
 
2.9%
27 17
 
2.4%
24 17
 
2.4%
23 17
 
2.4%
Other values (103) 464
66.3%
(Missing) 37
 
5.3%
ValueCountFrequency (%)
14 6
 
0.9%
15 8
 
1.1%
16 15
2.1%
17 13
1.9%
18 21
3.0%
19 13
1.9%
20 21
3.0%
21 23
3.3%
22 22
3.1%
23 17
2.4%
ValueCountFrequency (%)
446 1
0.1%
253 1
0.1%
249 1
0.1%
242 1
0.1%
234 1
0.1%
221 1
0.1%
220 1
0.1%
190 1
0.1%
186 1
0.1%
177 1
0.1%

debtinc
Real number (ℝ)

High correlation 

Distinct231
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.260571
Minimum0.4
Maximum41.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2025-10-06T16:37:20.718871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile1.9
Q15
median8.6
Q314.125
95-th percentile23.8
Maximum41.3
Range40.9
Interquartile range (IQR)9.125

Descriptive statistics

Standard deviation6.8272336
Coefficient of variation (CV)0.66538532
Kurtosis1.2185738
Mean10.260571
Median Absolute Deviation (MAD)4.2
Skewness1.0960633
Sum7182.4
Variance46.611118
MonotonicityNot monotonic
2025-10-06T16:37:20.949289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.5 10
 
1.4%
6.7 9
 
1.3%
5.4 9
 
1.3%
4.4 9
 
1.3%
7.2 8
 
1.1%
6.4 8
 
1.1%
6.6 8
 
1.1%
3.7 8
 
1.1%
13.2 7
 
1.0%
4.8 7
 
1.0%
Other values (221) 617
88.1%
ValueCountFrequency (%)
0.4 1
 
0.1%
0.6 2
0.3%
0.7 1
 
0.1%
0.8 1
 
0.1%
0.9 3
0.4%
1 1
 
0.1%
1.1 3
0.4%
1.2 4
0.6%
1.3 2
0.3%
1.4 2
0.3%
ValueCountFrequency (%)
41.3 1
0.1%
36.6 1
0.1%
35.3 1
0.1%
33.4 1
0.1%
33.3 1
0.1%
32.5 1
0.1%
30.8 1
0.1%
30.7 1
0.1%
30.6 1
0.1%
30.1 1
0.1%

creddebt
Real number (ℝ)

High correlation 

Distinct695
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5535528
Minimum0.011696
Maximum20.56131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2025-10-06T16:37:21.168157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.011696
5-th percentile0.1070292
Q10.36905925
median0.8548695
Q31.901955
95-th percentile5.0982645
Maximum20.56131
Range20.549614
Interquartile range (IQR)1.5328958

Descriptive statistics

Standard deviation2.117197
Coefficient of variation (CV)1.3628098
Kurtosis21.980021
Mean1.5535528
Median Absolute Deviation (MAD)0.5921385
Skewness3.898617
Sum1087.487
Variance4.4825231
MonotonicityNot monotonic
2025-10-06T16:37:21.372470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.402192 2
 
0.3%
0.105 2
 
0.3%
0.2254 2
 
0.3%
0.44982 2
 
0.3%
0.085785 2
 
0.3%
1.00584 1
 
0.1%
0.60204 1
 
0.1%
2.66262 1
 
0.1%
1.08528 1
 
0.1%
15.791776 1
 
0.1%
Other values (685) 685
97.9%
ValueCountFrequency (%)
0.011696 1
0.1%
0.014835 1
0.1%
0.024528 1
0.1%
0.024576 1
0.1%
0.025074 1
0.1%
0.029412 1
0.1%
0.029898 1
0.1%
0.030212 1
0.1%
0.030492 1
0.1%
0.030628 1
0.1%
ValueCountFrequency (%)
20.56131 1
0.1%
16.03147 1
0.1%
15.791776 1
0.1%
15.01668 1
0.1%
14.5962 1
0.1%
14.231448 1
0.1%
11.359392 1
0.1%
9.8766 1
0.1%
9.60048 1
0.1%
9.5934 1
0.1%

othdebt
Real number (ℝ)

High correlation 

Distinct699
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0582086
Minimum0.045584
Maximum27.0336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2025-10-06T16:37:21.579773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.045584
5-th percentile0.3767268
Q11.0441782
median1.9875675
Q33.9230647
95-th percentile9.5016481
Maximum27.0336
Range26.988016
Interquartile range (IQR)2.8788865

Descriptive statistics

Standard deviation3.2875545
Coefficient of variation (CV)1.0749935
Kurtosis10.32941
Mean3.0582086
Median Absolute Deviation (MAD)1.121652
Skewness2.7281631
Sum2140.746
Variance10.808015
MonotonicityNot monotonic
2025-10-06T16:37:21.831828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.8234 2
 
0.3%
5.008608 1
 
0.1%
0.56364 1
 
0.1%
0.606424 1
 
0.1%
1.13796 1
 
0.1%
4.02738 1
 
0.1%
2.72272 1
 
0.1%
23.104224 1
 
0.1%
1.258136 1
 
0.1%
9.97464 1
 
0.1%
Other values (689) 689
98.4%
ValueCountFrequency (%)
0.045584 1
0.1%
0.089488 1
0.1%
0.100926 1
0.1%
0.10752 1
0.1%
0.129582 1
0.1%
0.15012 1
0.1%
0.1563 1
0.1%
0.160983 1
0.1%
0.163863 1
0.1%
0.168102 1
0.1%
ValueCountFrequency (%)
27.0336 1
0.1%
23.104224 1
0.1%
20.615868 1
0.1%
18.26913 1
0.1%
18.257382 1
0.1%
17.79899 1
0.1%
17.2038 1
0.1%
17.184552 1
0.1%
16.668126 1
0.1%
15.40539 1
0.1%

default
Categorical

Imbalance 

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
0
515 
1
183 
'0'
 
1
:0
 
1

Length

Max length3
Median length1
Mean length1.0042857
Min length1

Characters and Unicode

Total characters703
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 515
73.6%
1 183
 
26.1%
'0' 1
 
0.1%
:0 1
 
0.1%

Length

2025-10-06T16:37:22.063997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-06T16:37:22.243505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 517
73.9%
1 183
 
26.1%

Most occurring characters

ValueCountFrequency (%)
0 517
73.5%
1 183
 
26.0%
' 2
 
0.3%
: 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 703
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 517
73.5%
1 183
 
26.0%
' 2
 
0.3%
: 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 703
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 517
73.5%
1 183
 
26.0%
' 2
 
0.3%
: 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 703
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 517
73.5%
1 183
 
26.0%
' 2
 
0.3%
: 1
 
0.1%

Interactions

2025-10-06T16:37:16.900169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:10.715539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:11.805666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:12.794096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:13.959890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:14.924372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:15.945433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:17.036438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:10.902238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:11.943455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:12.935616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:14.099437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:15.071426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:16.081252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:17.174636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:11.045639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:12.096109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:13.081213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:14.233785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:15.220731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:16.219869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:17.318641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:11.193591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:12.243229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:13.238985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:14.394902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:15.373141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:16.362617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:17.447585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:11.325186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:12.379282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:13.371013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:14.523410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:15.508395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:16.487555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:17.598059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:11.477000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:12.531439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:13.689453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:14.667890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:15.667714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:16.634994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:17.723753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:11.615049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:12.663347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:13.821297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:14.792917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:15.805211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-10-06T16:37:16.765626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2025-10-06T16:37:22.366900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
addressagecreddebtdebtincdefaultedemployincomeothdebt
address1.0000.5520.2470.0370.1490.0560.3020.3640.238
age0.5521.0000.3230.0070.0720.0000.5370.5900.341
creddebt0.2470.3231.0000.6240.0870.0330.3290.5090.622
debtinc0.0370.0070.6241.0000.2170.033-0.071-0.0310.734
default0.1490.0720.0870.2171.0000.0520.2030.0440.000
ed0.0560.0000.0330.0330.0521.0000.0730.1980.119
employ0.3020.5370.329-0.0710.2030.0731.0000.7090.341
income0.3640.5900.509-0.0310.0440.1980.7091.0000.530
othdebt0.2380.3410.6220.7340.0000.1190.3410.5301.000

Missing values

2025-10-06T16:37:18.107173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-06T16:37:18.325613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-10-06T16:37:18.564282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ageedemployaddressincomedebtinccreddebtothdebtdefault
041.03.01712176.09.311.3593925.0086081
127.01.010631.017.31.3622024.0007980
240.01.0157NaN5.50.8560752.1689250
341.0NaN1514120.02.92.6587200.8212800
424.02.02028.017.31.7874363.0565641
541.02.05525.010.20.3927002.1573000
639.01.0209NaN30.63.83387416.6681260
7NaN1.0121138.03.60.1285921.2394080
824.01.03419.024.41.3583483.2776521
936.01.001325.019.72.7777002.1473000
ageedemployaddressincomedebtinccreddebtothdebtdefault
69024.02.00516.07.30.0245281.1434720
69147.01.0318253.07.29.3083768.9076240
692NaN1.0026NaN28.92.7544595.0485411
69322.03.00220.04.70.2190200.7209800
69448.02.06166.012.12.3159405.6700600
69536.02.061527.04.60.2620620.9799381
69629.02.06421.011.50.3694952.0455050
69733.01.015332.07.60.4912641.9407360
69845.01.0192277.08.42.3026084.1653920
69937.01.01214NaN14.72.9946843.4733160